Hydrocarbon reservoirs are exploited by drilling wells in a hydrocarbon bearing geologic formation. Both producing wells and injecting wells are typically used. The role of producing wells (producers) is to allow hydrocarbons to flow to the surface. Injecting wells (injectors) are drilled in order to maintain the reservoir pressure by injecting fluids (typically water or gas) to replace the produced fluids.
The key to a successful exploitation operation of a petroleum reservoir is to efficiently design and operate wells. In order to guide and optimize well operations, simulators are often used. The role of reservoir simulators is to forecast the production of wells in order to evaluate the possible outcomes of operational changes.
Reservoir simulators can be created in a variety of ways, but for the purpose of production optimization, it is desirable to take an approach that is both fast and accurate. The accuracy of the simulator is defined as the predictive power of the simulator: its ability to predict future well performance accurately and with a high level of confidence. The simulator's accuracy helps guarantee the economic success of the operational changes implemented. The speed of the simulator is defined as the time it takes to create or update a model and to perform a simulation. A fast simulator is desirable to update the model with new data in order to support daily operational decisions in a timely fashion.
The standard approach followed in the petroleum industry to model reservoirs is to use grid-based reservoir simulators. These simulators often rely on a finite volume discretization of the equations governing the motion of reservoir fluids. Alternate discretization methods, such as finite element methods, are also used from time to time. These methods all have in common that the primary unknowns solved during the computation are the fluid pressures of each fluid phase and the composition of each fluid component.
Classical grid-based reservoir simulation can be very accurate but is usually prohibitively slow. These models are large and require significant computer resources to run them. They are prohibitively slow for use in supporting day-to-day decisions related to production optimization. Grid-based reservoir simulation models are used primarily to support long-term field development decisions, such as the addition of new wells or changes to the exploitation strategy of the field.
Reservoir production can also be affected by material travel between reservoir tanks. To account for this travel of material, equations such as the general material balance equation (GMBE) may be used. The general material balance equation relies on the following assumptions: the reservoir follows an isothermal transformation and is at equilibrium at any point in time; the gas component can be in solution in the oil phase; the oil component can be volatile in the gas phase; and rock, water, and hydrocarbons are (slightly) compressible. While useful for general material balance determinations, the GMBE lacks the specificity to be applicable in specific multi-tank situations.